Overview

Dataset statistics

Number of variables14
Number of observations5971
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory699.7 KiB
Average record size in memory120.0 B

Variable types

Numeric14

Warnings

purchases is highly correlated with quantity_p and 1 other fieldsHigh correlation
devolutions is highly correlated with quantity_p and 3 other fieldsHigh correlation
recency_p is highly correlated with avg_recency_daysHigh correlation
recency_d is highly correlated with invoices_dHigh correlation
quantity_p is highly correlated with purchases and 4 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 3 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 1 other fieldsHigh correlation
invoices_d is highly correlated with recency_d and 1 other fieldsHigh correlation
avg_ticket is highly correlated with devolutions and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_pHigh correlation
avg_basket_size is highly correlated with devolutions and 3 other fieldsHigh correlation
purchases is highly correlated with quantity_p and 3 other fieldsHigh correlation
devolutions is highly correlated with recency_d and 2 other fieldsHigh correlation
recency_p is highly correlated with invoices_p and 1 other fieldsHigh correlation
recency_d is highly correlated with devolutions and 2 other fieldsHigh correlation
quantity_p is highly correlated with purchases and 2 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 2 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 4 other fieldsHigh correlation
invoices_d is highly correlated with devolutions and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_p and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with purchases and 2 other fieldsHigh correlation
avg_variety is highly correlated with purchases and 1 other fieldsHigh correlation
purchases_pday is highly correlated with invoices_pHigh correlation
purchases is highly correlated with quantity_p and 2 other fieldsHigh correlation
devolutions is highly correlated with recency_d and 2 other fieldsHigh correlation
recency_p is highly correlated with invoices_p and 1 other fieldsHigh correlation
recency_d is highly correlated with devolutions and 2 other fieldsHigh correlation
quantity_p is highly correlated with purchases and 1 other fieldsHigh correlation
quantity_d is highly correlated with devolutions and 2 other fieldsHigh correlation
invoices_p is highly correlated with purchases and 1 other fieldsHigh correlation
invoices_d is highly correlated with devolutions and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_pHigh correlation
avg_basket_size is highly correlated with purchases and 2 other fieldsHigh correlation
avg_variety is highly correlated with avg_basket_sizeHigh correlation
invoices_d is highly correlated with invoices_p and 1 other fieldsHigh correlation
invoices_p is highly correlated with invoices_d and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with purchases and 4 other fieldsHigh correlation
recency_p is highly correlated with customer_id and 1 other fieldsHigh correlation
customer_id is highly correlated with recency_p and 1 other fieldsHigh correlation
purchases is highly correlated with invoices_d and 6 other fieldsHigh correlation
devolutions is highly correlated with avg_basket_size and 4 other fieldsHigh correlation
quantity_p is highly correlated with avg_basket_size and 4 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_size and 4 other fieldsHigh correlation
quantity_d is highly correlated with avg_basket_size and 4 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_p and 2 other fieldsHigh correlation
recency_d is highly correlated with avg_recency_daysHigh correlation
purchases is highly skewed (γ1 = 21.77363976) Skewed
devolutions is highly skewed (γ1 = 50.91642437) Skewed
quantity_p is highly skewed (γ1 = 35.09784254) Skewed
quantity_d is highly skewed (γ1 = 53.23013972) Skewed
avg_ticket is highly skewed (γ1 = 51.96108487) Skewed
avg_basket_size is highly skewed (γ1 = 49.85733829) Skewed
customer_id has unique values Unique
purchases has 215 (3.6%) zeros Zeros
devolutions has 4201 (70.4%) zeros Zeros
quantity_p has 215 (3.6%) zeros Zeros
quantity_d has 4201 (70.4%) zeros Zeros
invoices_p has 215 (3.6%) zeros Zeros
invoices_d has 4201 (70.4%) zeros Zeros
avg_ticket has 215 (3.6%) zeros Zeros
avg_basket_size has 215 (3.6%) zeros Zeros
avg_variety has 215 (3.6%) zeros Zeros
purchases_pday has 215 (3.6%) zeros Zeros

Reproduction

Analysis started2021-06-05 20:45:49.054417
Analysis finished2021-06-05 20:46:18.248381
Duration29.19 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5971
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16765.63189
Minimum12346
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:18.359518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12711
Q114369.5
median16392
Q319241.5
95-th percentile21892.5
Maximum22709
Range10363
Interquartile range (IQR)4872

Descriptive statistics

Standard deviation2882.537033
Coefficient of variation (CV)0.1719313088
Kurtosis-0.9581116481
Mean16765.63189
Median Absolute Deviation (MAD)2180
Skewness0.3706824238
Sum100107588
Variance8309019.745
MonotonicityNot monotonic
2021-06-05T17:46:18.514343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163841
 
< 0.1%
156651
 
< 0.1%
156771
 
< 0.1%
217671
 
< 0.1%
177221
 
< 0.1%
156731
 
< 0.1%
133221
 
< 0.1%
177181
 
< 0.1%
156691
 
< 0.1%
214841
 
< 0.1%
Other values (5961)5961
99.8%
ValueCountFrequency (%)
123461
< 0.1%
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
ValueCountFrequency (%)
227091
< 0.1%
227081
< 0.1%
227071
< 0.1%
227061
< 0.1%
227051
< 0.1%
227041
< 0.1%
227001
< 0.1%
226991
< 0.1%
226961
< 0.1%
226951
< 0.1%

purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct5552
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1785.401859
Minimum0
Maximum280206.02
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:18.663220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.75
Q1206.585
median599.97
Q31588.97
95-th percentile5393.625
Maximum280206.02
Range280206.02
Interquartile range (IQR)1382.385

Descriptive statistics

Standard deviation7789.345865
Coefficient of variation (CV)4.362796995
Kurtosis620.2010104
Mean1785.401859
Median Absolute Deviation (MAD)495.15
Skewness21.77363976
Sum10660634.5
Variance60673909
MonotonicityNot monotonic
2021-06-05T17:46:18.809375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0215
 
3.6%
7.959
 
0.2%
1.258
 
0.1%
2.958
 
0.1%
4.958
 
0.1%
12.757
 
0.1%
1.657
 
0.1%
3.757
 
0.1%
7.56
 
0.1%
4.256
 
0.1%
Other values (5542)5690
95.3%
ValueCountFrequency (%)
0215
3.6%
0.421
 
< 0.1%
0.551
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.843
 
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.11
 
< 0.1%
1.258
 
0.1%
ValueCountFrequency (%)
280206.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
143825.061
< 0.1%
124914.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
81024.841
< 0.1%
77183.61
< 0.1%

devolutions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1325
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.19206
Minimum0
Maximum168469.6
Zeros4201
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:18.960568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39.5
95-th percentile219.265
Maximum168469.6
Range168469.6
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation2602.83106
Coefficient of variation (CV)17.33001771
Kurtosis3072.075434
Mean150.19206
Median Absolute Deviation (MAD)0
Skewness50.91642437
Sum896796.79
Variance6774729.525
MonotonicityNot monotonic
2021-06-05T17:46:19.111193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04201
70.4%
12.7522
 
0.4%
4.9519
 
0.3%
1517
 
0.3%
9.9515
 
0.3%
5.913
 
0.2%
25.511
 
0.2%
4.2510
 
0.2%
3.759
 
0.2%
19.99
 
0.2%
Other values (1315)1645
 
27.5%
ValueCountFrequency (%)
04201
70.4%
0.422
 
< 0.1%
0.651
 
< 0.1%
0.771
 
< 0.1%
0.951
 
< 0.1%
11
 
< 0.1%
1.256
 
0.1%
1.454
 
0.1%
1.641
 
< 0.1%
1.655
 
0.1%
ValueCountFrequency (%)
168469.61
< 0.1%
77183.61
< 0.1%
392671
< 0.1%
30032.231
< 0.1%
22998.41
< 0.1%
17836.461
< 0.1%
16888.021
< 0.1%
16453.711
< 0.1%
13541.332
< 0.1%
13474.791
< 0.1%

recency_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.0015073
Minimum0
Maximum373
Zeros38
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:19.262332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median77
Q3215
95-th percentile365
Maximum373
Range373
Interquartile range (IQR)191

Descriptive statistics

Standard deviation118.7308916
Coefficient of variation (CV)0.9422973912
Kurtosis-0.8103832322
Mean126.0015073
Median Absolute Deviation (MAD)67
Skewness0.7488472968
Sum752355
Variance14097.02462
MonotonicityNot monotonic
2021-06-05T17:46:19.410370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365234
 
3.9%
1110
 
1.8%
4105
 
1.8%
399
 
1.7%
292
 
1.5%
1086
 
1.4%
882
 
1.4%
980
 
1.3%
1779
 
1.3%
778
 
1.3%
Other values (294)4926
82.5%
ValueCountFrequency (%)
038
 
0.6%
1110
1.8%
292
1.5%
399
1.7%
4105
1.8%
552
0.9%
778
1.3%
882
1.4%
980
1.3%
1086
1.4%
ValueCountFrequency (%)
37323
 
0.4%
37223
 
0.4%
37117
 
0.3%
3694
 
0.1%
36813
 
0.2%
36718
 
0.3%
36615
 
0.3%
365234
3.9%
36411
 
0.2%
3627
 
0.1%

recency_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct281
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.9606431
Minimum0
Maximum373
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:19.565197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q1282
median365
Q3365
95-th percentile365
Maximum373
Range373
Interquartile range (IQR)83

Descriptive statistics

Standard deviation120.0996751
Coefficient of variation (CV)0.4030722777
Kurtosis0.4613608502
Mean297.9606431
Median Absolute Deviation (MAD)0
Skewness-1.463353946
Sum1779123
Variance14423.93195
MonotonicityNot monotonic
2021-06-05T17:46:19.715871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3654215
70.6%
845
 
0.8%
6439
 
0.7%
4631
 
0.5%
2131
 
0.5%
3528
 
0.5%
328
 
0.5%
927
 
0.5%
2523
 
0.4%
2922
 
0.4%
Other values (271)1482
 
24.8%
ValueCountFrequency (%)
05
 
0.1%
120
0.3%
213
 
0.2%
328
0.5%
415
 
0.3%
54
 
0.1%
710
 
0.2%
845
0.8%
927
0.5%
108
 
0.1%
ValueCountFrequency (%)
3731
 
< 0.1%
3728
 
0.1%
3712
 
< 0.1%
3692
 
< 0.1%
3689
 
0.2%
36711
 
0.2%
3667
 
0.1%
3654215
70.6%
3642
 
< 0.1%
3622
 
< 0.1%

quantity_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct817
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.9107352
Minimum0
Maximum80996
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:19.876155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q134
median91
Q3200
95-th percentile631.5
Maximum80996
Range80996
Interquartile range (IQR)166

Descriptive statistics

Standard deviation1701.491022
Coefficient of variation (CV)6.674850396
Kurtosis1502.456269
Mean254.9107352
Median Absolute Deviation (MAD)70
Skewness35.09784254
Sum1522072
Variance2895071.697
MonotonicityNot monotonic
2021-06-05T17:46:20.029531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1295
 
4.9%
0215
 
3.6%
3117
 
2.0%
653
 
0.9%
2850
 
0.8%
2148
 
0.8%
1648
 
0.8%
6742
 
0.7%
5241
 
0.7%
3641
 
0.7%
Other values (807)5021
84.1%
ValueCountFrequency (%)
0215
3.6%
1295
4.9%
226
 
0.4%
3117
 
2.0%
426
 
0.4%
521
 
0.4%
653
 
0.9%
725
 
0.4%
819
 
0.3%
917
 
0.3%
ValueCountFrequency (%)
809961
< 0.1%
742151
< 0.1%
386391
< 0.1%
213521
< 0.1%
173761
< 0.1%
171501
< 0.1%
162881
< 0.1%
158531
< 0.1%
133691
< 0.1%
128721
< 0.1%

quantity_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct189
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.92095126
Minimum-0
Maximum80995
Zeros4201
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:20.189116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile-0
Q1-0
median-0
Q31
95-th percentile28
Maximum80995
Range80995
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1435.845873
Coefficient of variation (CV)35.08828189
Kurtosis2885.966697
Mean40.92095126
Median Absolute Deviation (MAD)0
Skewness53.23013972
Sum244339
Variance2061653.371
MonotonicityNot monotonic
2021-06-05T17:46:20.340315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-04201
70.4%
1512
 
8.6%
3174
 
2.9%
292
 
1.5%
690
 
1.5%
477
 
1.3%
546
 
0.8%
1246
 
0.8%
742
 
0.7%
840
 
0.7%
Other values (179)651
 
10.9%
ValueCountFrequency (%)
-04201
70.4%
1512
 
8.6%
292
 
1.5%
3174
 
2.9%
477
 
1.3%
546
 
0.8%
690
 
1.5%
742
 
0.7%
840
 
0.7%
937
 
0.6%
ValueCountFrequency (%)
809951
< 0.1%
742151
< 0.1%
93611
< 0.1%
90141
< 0.1%
48731
< 0.1%
40271
< 0.1%
23991
< 0.1%
23021
< 0.1%
21601
< 0.1%
16851
< 0.1%

invoices_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct60
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.339976553
Minimum0
Maximum209
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:20.502560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum209
Range209
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.736955851
Coefficient of variation (CV)2.01706681
Kurtosis316.395646
Mean3.339976553
Median Absolute Deviation (MAD)1
Skewness13.45819778
Sum19943
Variance45.38657414
MonotonicityNot monotonic
2021-06-05T17:46:20.645142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12916
48.8%
2831
 
13.9%
3508
 
8.5%
4387
 
6.5%
5242
 
4.1%
0215
 
3.6%
6172
 
2.9%
7143
 
2.4%
898
 
1.6%
968
 
1.1%
Other values (50)391
 
6.5%
ValueCountFrequency (%)
0215
 
3.6%
12916
48.8%
2831
 
13.9%
3508
 
8.5%
4387
 
6.5%
5242
 
4.1%
6172
 
2.9%
7143
 
2.4%
898
 
1.6%
968
 
1.1%
ValueCountFrequency (%)
2091
< 0.1%
2011
< 0.1%
1241
< 0.1%
971
< 0.1%
931
< 0.1%
911
< 0.1%
861
< 0.1%
731
< 0.1%
631
< 0.1%
621
< 0.1%

invoices_d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6421035003
Minimum0
Maximum47
Zeros4201
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:20.769958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum47
Range47
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.861996514
Coefficient of variation (CV)2.899838598
Kurtosis174.8938214
Mean0.6421035003
Median Absolute Deviation (MAD)0
Skewness10.06208452
Sum3834
Variance3.467031018
MonotonicityNot monotonic
2021-06-05T17:46:20.887838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
04201
70.4%
11068
 
17.9%
2308
 
5.2%
3147
 
2.5%
497
 
1.6%
544
 
0.7%
630
 
0.5%
722
 
0.4%
89
 
0.2%
97
 
0.1%
Other values (17)38
 
0.6%
ValueCountFrequency (%)
04201
70.4%
11068
 
17.9%
2308
 
5.2%
3147
 
2.5%
497
 
1.6%
544
 
0.7%
630
 
0.5%
722
 
0.4%
89
 
0.2%
97
 
0.1%
ValueCountFrequency (%)
471
< 0.1%
451
< 0.1%
351
< 0.1%
311
< 0.1%
271
< 0.1%
231
< 0.1%
211
< 0.1%
192
< 0.1%
181
< 0.1%
172
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct5576
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.59449396
Minimum0
Maximum77183.6
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:21.020751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.918888889
Q17.920322992
median15.7246
Q322.25928571
95-th percentile79.2140625
Maximum77183.6
Range77183.6
Interquartile range (IQR)14.33896272

Descriptive statistics

Standard deviation1274.292949
Coefficient of variation (CV)21.02984719
Kurtosis2881.449309
Mean60.59449396
Median Absolute Deviation (MAD)7.4314
Skewness51.96108487
Sum361809.7234
Variance1623822.519
MonotonicityNot monotonic
2021-06-05T17:46:21.172018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0215
 
3.6%
3.7511
 
0.2%
4.9510
 
0.2%
2.959
 
0.2%
1.259
 
0.2%
7.958
 
0.1%
12.757
 
0.1%
8.257
 
0.1%
1.657
 
0.1%
5.956
 
0.1%
Other values (5566)5682
95.2%
ValueCountFrequency (%)
0215
3.6%
0.422
 
< 0.1%
0.5351
 
< 0.1%
0.551
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.83714285711
 
< 0.1%
0.842
 
< 0.1%
0.853
 
0.1%
1.0022222221
 
< 0.1%
ValueCountFrequency (%)
77183.61
< 0.1%
56157.51
< 0.1%
13541.331
< 0.1%
13305.51
< 0.1%
11062.061
< 0.1%
4453.431
< 0.1%
4287.631
< 0.1%
38611
< 0.1%
30961
< 0.1%
2653.951
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1280
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.87658
Minimum0
Maximum373
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:21.329848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q139.63333333
median80
Q3182.5
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)142.8666667

Descriptive statistics

Standard deviation102.9113998
Coefficient of variation (CV)0.8513758398
Kurtosis-0.2161395638
Mean120.87658
Median Absolute Deviation (MAD)53.3
Skewness0.9688375366
Sum721754.0593
Variance10590.75621
MonotonicityNot monotonic
2021-06-05T17:46:21.486516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4637
 
0.6%
5334
 
0.6%
3933
 
0.6%
2832
 
0.5%
6032
 
0.5%
35331
 
0.5%
21329
 
0.5%
36728
 
0.5%
10628
 
0.5%
18428
 
0.5%
Other values (1270)5659
94.8%
ValueCountFrequency (%)
04
 
0.1%
111
0.2%
27
0.1%
2.5547945211
 
< 0.1%
314
0.2%
3.2434782611
 
< 0.1%
3.3008849561
 
< 0.1%
3.3333333331
 
< 0.1%
3.51
 
< 0.1%
3.6666666671
 
< 0.1%
ValueCountFrequency (%)
37321
0.4%
37222
0.4%
37118
0.3%
3694
 
0.1%
36814
0.2%
36728
0.5%
36613
0.2%
36519
0.3%
36411
 
0.2%
3627
 
0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2372
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.1652064
Minimum0
Maximum74215
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:21.645628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q164
median142.6666667
Q3282
95-th percentile718
Maximum74215
Range74215
Interquartile range (IQR)218

Descriptive statistics

Standard deviation1170.070864
Coefficient of variation (CV)4.603583947
Kurtosis2915.667275
Mean254.1652064
Median Absolute Deviation (MAD)98.66666667
Skewness49.85733829
Sum1517620.447
Variance1369065.827
MonotonicityNot monotonic
2021-06-05T17:46:21.793306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0215
 
3.6%
1168
 
2.8%
272
 
1.2%
354
 
0.9%
451
 
0.9%
536
 
0.6%
628
 
0.5%
1226
 
0.4%
7321
 
0.4%
10021
 
0.4%
Other values (2362)5279
88.4%
ValueCountFrequency (%)
0215
3.6%
1168
2.8%
1.51
 
< 0.1%
272
 
1.2%
354
 
0.9%
3.3333333331
 
< 0.1%
451
 
0.9%
536
 
0.6%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
ValueCountFrequency (%)
742151
< 0.1%
40498.51
< 0.1%
141491
< 0.1%
139561
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
59641
< 0.1%
51981
< 0.1%
43001
< 0.1%
42801
< 0.1%

avg_variety
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1279
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.20798644
Minimum0
Maximum1114
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:21.949925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17.5
median17
Q334
95-th percentile171
Maximum1114
Range1114
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation75.8309033
Coefficient of variation (CV)1.984687244
Kurtosis33.55299436
Mean38.20798644
Median Absolute Deviation (MAD)11.75
Skewness5.086636807
Sum228139.887
Variance5750.325895
MonotonicityNot monotonic
2021-06-05T17:46:22.097168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1331
 
5.5%
0215
 
3.6%
2164
 
2.7%
3114
 
1.9%
13102
 
1.7%
1097
 
1.6%
1496
 
1.6%
496
 
1.6%
595
 
1.6%
994
 
1.6%
Other values (1269)4567
76.5%
ValueCountFrequency (%)
0215
3.6%
1331
5.5%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
< 0.1%
1.59
 
0.2%
1.5555555561
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
ValueCountFrequency (%)
11141
< 0.1%
7491
< 0.1%
7311
< 0.1%
7211
< 0.1%
7051
< 0.1%
6871
< 0.1%
6761
< 0.1%
6751
< 0.1%
6621
< 0.1%
6511
< 0.1%

purchases_pday
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1243
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5312110211
Minimum0
Maximum17
Zeros215
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2021-06-05T17:46:22.245666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.007782218992
Q10.02285714286
median0.6666666667
Q31
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0.9771428571

Descriptive statistics

Standard deviation0.5507143751
Coefficient of variation (CV)1.03671489
Kurtosis132.9126315
Mean0.5312110211
Median Absolute Deviation (MAD)0.5
Skewness4.713845887
Sum3171.861007
Variance0.3032863229
MonotonicityNot monotonic
2021-06-05T17:46:22.396782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12925
49.0%
0215
 
3.6%
250
 
0.8%
0.062518
 
0.3%
0.0277777777817
 
0.3%
0.0238095238117
 
0.3%
0.0909090909115
 
0.3%
0.0833333333314
 
0.2%
0.0294117647113
 
0.2%
0.0769230769213
 
0.2%
Other values (1233)2674
44.8%
ValueCountFrequency (%)
0215
3.6%
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
 
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
 
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
42
 
< 0.1%
34
 
0.1%
250
 
0.8%
1.1428571431
 
< 0.1%
12925
49.0%
0.751
 
< 0.1%
0.66666666674
 
0.1%
0.55882352941
 
< 0.1%
0.53887399461
 
< 0.1%

Interactions

2021-06-05T17:45:51.450929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:51.576057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:51.694417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:51.822026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:51.946169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:52.068426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:52.191490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:52.319991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:52.434865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:52.550131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:52.675024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:52.796125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:52.921114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:53.038449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:53.161033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:53.277263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:53.394080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:53.518563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:53.639436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:53.759478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:53.883573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:54.006824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:54.119915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:54.233384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:54.361267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:54.491720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:54.615293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:54.732919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:54.856948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:55.634523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:55.760813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:55.891282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:56.024061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:56.160866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:56.299021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:56.431728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:56.554960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:56.676282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:56.811243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:56.939512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:57.076902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:57.198678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:57.329345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:57.455494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:57.579312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:57.710103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:57.838993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:57.963844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:58.092170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:58.225099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:58.351539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:58.472740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:58.603562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:58.729844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:58.858577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:58.982698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:59.109175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:59.234281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:59.361451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:59.496469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:59.630733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:59.767252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:45:59.918631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:00.060443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:00.184301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:00.316599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:00.477165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:00.619694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:00.754907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:00.875324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:01.204388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:01.337353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:01.463535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:01.596960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:01.730095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:01.861322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:01.996220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:02.136922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:02.259310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:02.382211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:02.519245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:02.651141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:02.781848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:02.908407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:03.042098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:03.171776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:03.303887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:03.439226image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:03.580390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:03.710925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:03.844087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:03.980278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:04.104565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:04.228384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:04.363167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:04.496014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:04.629515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:04.758826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:04.898511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:05.010287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:05.124950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:05.243229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:05.363871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:05.485591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:05.608238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:05.729202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:05.839231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:05.959105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:06.081378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:06.199626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:06.321540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:06.439288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:06.565852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:06.680144image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:06.790905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:06.906042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:07.026056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:07.144692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:07.263111image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:07.383973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:07.493635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:07.599761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:07.957317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:08.075021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:08.191883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:08.302549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:08.423830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:08.553430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:08.679526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:08.816398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:08.956156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:09.087970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:09.225776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:09.361249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:09.490632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:09.622360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:09.757511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:09.890168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:10.026922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:10.157418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:10.292983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:10.418167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:10.542233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:10.673753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:10.806157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:10.941753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:11.080131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:11.209282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:11.335619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:11.456017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:11.586341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:11.715641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:11.845326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:11.979584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:12.109243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:12.232349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:12.356928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:12.490204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:12.619604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:12.747926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:12.880442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:13.010030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:13.137421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:13.259451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:13.389708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:13.518496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:13.647954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:13.772651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:13.908373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:14.024351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:14.141384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:14.261300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:14.380525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:14.498265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:14.621382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:14.749509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:14.865368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:14.979650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:15.105504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:15.226254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:15.353385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:15.469253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:15.591046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:15.716594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:15.840890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:16.274760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:16.407578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:16.537145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:16.671394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:16.809259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:16.932699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:17.055496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:17.188554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:17.322800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:17.456207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-05T17:46:17.580452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-06-05T17:46:22.545503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-05T17:46:22.748395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-05T17:46:22.950731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-05T17:46:23.154050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-05T17:46:17.828319image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-05T17:46:18.143937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

customer_idpurchasesdevolutionsrecency_precency_dquantity_pquantity_dinvoices_pinvoices_davg_ticketavg_recency_daysavg_basket_sizeavg_varietypurchases_pday
0178505391.21102.58372.0302.035.021.034.01.018.152222124.33333350.9705888.73529417.000000
1130473237.54158.4431.031.0132.06.010.08.018.82290726.642857139.10000017.2000000.029155
2125837281.3894.042.056.01569.050.015.03.029.47927120.722222337.33333316.4666670.040323
313748948.250.0095.0365.0169.0-0.05.00.033.86607193.25000087.8000005.6000000.017921
415100876.00240.90333.0330.048.022.03.03.0292.00000062.16666726.6666671.0000000.073171
5152914668.3071.7925.0172.0508.027.015.05.045.32330121.941176140.2000006.8666670.042980
6146885630.87523.497.07.0579.0281.021.06.017.21978617.761905172.42857115.5714290.057221
7178095411.91784.2916.016.0961.041.012.03.088.71983631.083333171.4166675.0833330.033520
81531160767.901348.560.00.02167.0231.091.027.025.5434644.098901419.71428626.1428570.243316
9145278508.82797.442.08.0198.03.055.031.08.7539305.82812537.98181817.6727270.149457

Last rows

customer_idpurchasesdevolutionsrecency_precency_dquantity_pquantity_dinvoices_pinvoices_davg_ticketavg_recency_daysavg_basket_sizeavg_varietypurchases_pday
5961227004839.420.01.0365.0917.0-0.01.00.078.0551611.01074.062.01.0
596213298360.000.01.0365.096.0-0.01.00.0180.0000001.096.02.01.0
596314569227.390.01.0365.070.0-0.01.00.018.9491671.079.012.01.0
59642270417.900.01.0365.02.0-0.01.00.02.5571431.014.07.01.0
5965227053.350.01.0365.01.0-0.01.00.01.6750001.02.02.01.0
5966227066637.590.01.0365.0430.0-0.01.00.010.4528981.01748.0635.01.0
5967227077689.230.00.0365.0347.0-0.01.00.010.5187820.02011.0731.01.0
5968227083217.200.00.0365.0524.0-0.01.00.054.5288140.0654.059.01.0
5969227095664.890.00.0365.0211.0-0.01.00.025.9857340.0732.0218.01.0
597012713848.550.00.0365.0101.0-0.01.00.022.3302630.0508.038.01.0